The future of software delivery is lean teams. A large team spends its weeks in alignment: frontend and backend agreeing on contracts, business and analysts agreeing on requirements, and everyone waiting on QA before a release. Every hand-off costs time, adds a place for the requirement to get lost, and puts another salary on the invoice. Two senior architects and the agents we run cover the whole chain, so a decision goes from conversation to code without waiting on anyone else, with the test gate and observability doing the checking a hired team used to. Faster, cheaper, and with far less that can be misunderstood on the way. Ten years of Java and Spring in regulated and e-commerce systems, and our own platform in production every day.
Google's DORA research put it plainly in 2025: AI "magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones." Agents write in the style of the codebase they land in. Drop one into a clear architecture with written requirements, tests and a disciplined release process, and it reproduces those patterns at speed. Drop it into the opposite and it reproduces that too, faster than anyone can review or fix. The 2026 data shows which way most teams are heading: duplicated code up 81% since 2023, refactoring down to under 4% of changes, 88% of developers watching AI add to their technical debt. This is why the foundation has to come first, ideally before the first agent runs: the patterns you set on day one are the ones every generated line will copy. It is also why the team can stay small. Every layer a company adds around its engineers, the manager relaying requirements, the QA team, the support tier, exists to compensate for a piece of foundation that is missing. Put the foundation in, and the work those layers did is still done, by the foundation itself, without hiring for it.
Requirements written down with acceptance criteria, and every decision recorded with the reason behind it. If it is not written, it is not agreed.
With AI on top The agent builds exactly what was written, so the architects review what was written before it does, and review what came out after.
What you stop paying for A manager whose job is relaying what you meant, and the rework when it arrived wrong.
Clear structure, explicit boundaries between parts, and diagrams that live next to the code, all set before the first agent runs. The patterns fixed on day one are the ones every generated line copies.
With AI on top An agent imitates the codebase it is given. Consistent patterns in, consistent code out. And a picture of the system is what lets you judge its work in minutes.
What you stop paying for The weeks every new person spends finding out where things are, and the rebuild when nobody can.
A test suite that fails when it should, releases small enough to roll back in minutes, and monitoring that names what broke instead of a generic error.
With AI on top Fewer than half of developers say they always verify AI output before committing. At volume, the safety net has to be automatic.
What you stop paying for A hired QA team. What we promised is what we check, before it ships: the test gate, the agents and our own review confirm that what we said would work does, at a fraction of the cost of staffing for it. Same for the release manager and the on-call tier.
What agents may touch, which data and secrets they may see, which dependencies they may add, who reviews their output, and what "done" means for generated code.
With AI on top Ambiguity creates risk. A clear stance turns a toy into a tool the whole team uses the same way.
What you stop paying for The review bottleneck, and the AI enablement role that large companies now hire for.
Each of these is done by the two of us and the agents we run. No project managers, no account layer between you and the people doing the work, and quality assured by default, by the test gate, the agents and our own review, rather than by a team you would have to hire. That is not a constraint we work around; it is the point.
A system built foundation first, then with agents on top, by the two architects who designed it. Java and Spring, a monolith or services as the problem dictates, delivered end to end from the first conversation to production without hand-offs in between. What we promised is what we test, and what we test is what ships.
You already have a codebase an agent or a small team generated. We read it as architects: what it costs to run, what breaks when the traffic grows, where the data model will not survive the next feature, and what to fix first. A written verdict, in plain language, with the fixes priced. This is the Cost and Risk Map.
For teams that want the agents but not the debt. We put in the foundation the agents will copy: the written rules they read before every task, the test gate that rejects what review misses, the release process, the AI stance. Then we measure it on delivery stability and time to production, not on how many people opened the tool.
Ongoing access to the two of us while your team and its agents keep building. We see changes before they ship, make the decisions agents will not make, cost, scale, data, and keep the foundation from drifting as the codebase grows. Month to month, no layers in between. This is the Retainer.
A few of the systems behind the claims above. Client work is described in general terms, without names or code.
A regulated enterprise client, a delivery in serious danger of failing. We reset the technical approach, re-scoped and re-planned the delivery, and stabilised release quality until the client's confidence came back. On the strength of that recovery the client awarded three further projects.
Technical lead for a global SAP Commerce Cloud platform: a team of 10+ Java developers, releases where a single bad deploy was not an option, and payment integrations across EMEA, APAC and North America, from Adyen and Stripe to WeChat, Alipay, UPI and Klarna. Custom features carved out of the monolith into microservices when the project needed it.
A platform where every principle on this page runs in production. Enter an address and get a due-diligence report on the property: land-registry records, encumbrances, risks and market context, assembled from public sources and written in plain language. Next to it, a map-based marketplace where properties are compared side by side against real market data instead of a listing price. Work that used to take a specialist days is done in minutes. Two people designed, built and run it, with coding agents doing much of the writing and the test gate and our own review deciding what ships.
We run coding agents on our own codebase daily, and we know exactly what it takes: written conventions the agent reads before every task, contracts it cannot break, a test suite that rejects what review misses, and audits that check its output against the code that actually runs. That practice is what we bring to your team, not a slide about the future of work.
HYCAD Software is a two-person studio built for how software gets delivered now: two senior architects and the agents we run, instead of a team of twelve and the meetings it takes to keep them aligned. The two of us spent the last decade on systems where a bad release costs real money: regulated enterprise platforms, a global e-commerce platform serving every continent, and now our own due-diligence product, which we build the same way we would build yours.
We start with the foundation: written requirements, clear boundaries, a test gate, observability. Then we put agents on top of it. Because the foundation does the work the extra layers used to do, there is nobody between you and the people designing and building your system, and nothing on the invoice that is not building it. We measure ourselves by outcomes you can point to: releases that stopped hurting, a system your team can still read, decisions you can defend.
HYCAD is run by the two people who also write the code. The person you talk to in the first call is the one who designs your system, ships it, and picks up the phone when something needs attention.
Co-founder
Takes a requirement from the first conversation to a system running in production: clarifies what is actually needed, designs the architecture, writes the code, and stays responsible for it after launch. Over a decade of production software for businesses where downtime has a price.
Co-founder
Turns loosely defined requirements into working software: breaks them into clear, testable pieces, builds the integrations and data flows behind them, and keeps iterating with the client until the system fits the way the team really works.
Each of these is its own product with its own page, its own price and its own terms. This page is the company behind them.
Two of us read an AI-generated codebase and write down what it costs to run, what will not survive growth, and what to fix first. A written verdict in three working days. Fixed price, quoted before we start; nothing found, nothing paid.
Request a review OpenThe fixes from the Map, done. The bill comes down and we show you the before and after, a deploy someone other than the author can run, a rollback tested in front of you, a backup restored, logging that names what broke. Ten working days, list agreed on day one.
See the sprint OpenOngoing access to the two of us while your team and its agents keep building: changes reviewed before they ship, the decisions agents will not make, the foundation kept from drifting. Month to month, no minimum term.
Talk about a retainerFor a specific offer, the pages above go straight to it. For anything else, an architecture question, a team that is stuck, a system nobody wants to touch, this reaches both of us.